US20090070350A1 - Collaborative data and knowledge integration - Google Patents

Abstract

A framework for to enable collaborative research includes enabling scientific researchers to dynamically interact with others, collaboratively author, annotate, review, comment on others' data, and discuss their research. The framework allows for providing a dynamic collaboration environment by harnessing collective contributions from a group of researchers and facilitating broader participation of people into research projects. The framework is a Web-based collaborative platform to dynamically integrate information for scientific research. Besides data integration, the system provides essential collaboration capabilities to boost user participation and collaboration. The system will not only enable users as active information contributors to bring significant new values to the data, but also provide an environment for scientific researchers to do collaborative research in a large research community.

Description

• This application claims the benefit of U.S. Provisional Application No. 60/970,627 filed Sep. 7, 2007, which is incorporated herein by reference.
BACKGROUND OF THE INVENTION
• The present invention relates generally to collaborative data integration and more particularly to collaborative information systems.
• Scientific research has become increasingly reliant on collaborative effort among multiple institutions and interdisciplinary consortia which share scientific experiments and data and collaborate on analysis of data and results. Traditional data management and integration systems focus on passively integrating existing data. Thus, the collaboration among data providers and users is limited.
• For example, the increased complexity of biomedical problems requires collaborative effort from multiple institutions and interdisciplinary consortia. The National Institutes of Health (NIH) provide large-scale collaborative project awards for teams of independently funded investigators to synergize and integrate their efforts. In this way, consortia are formed to pool expertise, validate approaches, forge common instrumentation platforms and rapidly translate new technologies toward clinical trials.
• One example is the “Networks for Translational Research: Optical Imaging” (NTROI), which was structured to support four multi-site teams that would include broad national and international representation from academia, NIH intramural, and device and drug industry investigators. One team works on breast cancer research with Multi-Dimensional Diffuse Optical Imaging. The consortium consists of six research programs across multiple universities and hospitals and includes nearly one hundred researchers. Together with three other teams, there are several hundred researchers working on the problems of optical imaging. The researchers not located at the same sites are limited in how closely they are able to collaborate due to the distributed nature of such large scale research consortia.
• However, current data integration systems only provide passive integration of existing data sources—a bottom up approach. There are several problems with this approach. First, schemas of data sources continue to evolve throughout the investigative process. This can disrupt integration. Second, data providers are generally not aware of changes and updates from other data sources. Also, they generally cannot contribute to such changes. That is, they are isolated to their own research and it is difficult for them to participate in collaboration with other researchers. Third, data users can only retroactively use data provided by others. The data users cannot proactively participate in active discussion, data reviewing, data authoring, or schema definition with other members due to the passive nature of traditional data integration. The lack of active collaboration can also cause disagreement in collaborative data sharing.
• The distribution and large scale of scientific data also poses new challenges for scientific data management and integration. The warehouse based approach is difficult. Collecting large amount of image data over the Internet can be very slow. Additionally, researchers generally prefer having control of their data on a server located on their own labs instead of storing the data elsewhere. As a result, without pushing collaboration into a collaborative environment, information is becoming further isolated.
• Further, the evolution of Web technology is transiting to a new paradigm. The term Web 2.0 refers to a second generation of services available on the World Wide Web that let people collaborate and share information online. For example, product purchasing sites (e.g., booksellers, clothiers, etc.) use users and/or readers as contributors, contributory knowledge sites (e.g., wikipedia, etc.) allow all content to be authored by users, weblogs generate content through participation (e.g., comments, etc.) instead of publishing only, and peer-to-peer file sharing sites (e.g., USENET, Bit-Torrent, Gnutella, FastTrack, etc.) radically decentralize data and the systems work by large scale participation of users. The Web is now shifting to strong interaction, participation, trust, and decentralization.
• Therefore, for such large scale networks of research, a collaborative environment and data integration system for researchers to easily manage, collaborate, share, and review their experiments and results is needed.
BRIEF SUMMARY OF THE INVENTION
• The present invention provides methods and systems for collaborative data management. A system for collaborative data management includes a central server with a plurality of collaborative engines. The central server receives information from a plurality of local servers and coordinates information transfer between the plurality of local servers and between the local servers and the central server. The central server's collaborative engines include a metadata engine, a schema engine, a hierarchy engine, and a messaging engine.
• The local servers each have similar local collaborative engines, the plurality of local servers adapted to transfer information to the central server and the plurality of local servers via the central server.
• These and other advantages of the invention will be apparent to those of ordinary skill in the art by reference to the following detailed description and the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
• FIG. 1 depicts a collaboration system according to an embodiment of the present invention;
• FIG. 2 depicts a schematic diagram of a portion ofcollaboration system100 according to an embodiment of the present invention; and
• FIG. 3 is a schematic drawing of a computer.
DETAILED DESCRIPTION
• The present invention generally provides methods and apparatus for a Web-based collaboration platform. A collaborative information integration platform enables sharing and co-authoring of schemas to achieve agreement on common data structures and semantics. It also facilitates collaborative tagging of data to improve search and personal organization of data through adding free tags or semantic tags. Users of a collaborative information integration platform may collaboratively review and analyze data to help improve the quality of data and experiments. The collaborative information integration platform also enables collaborative authoring of data to generate complete results and collaborative annotating of images with a Web-based tool to support collaborative annotation from multiple users. Additionally, personalized message exchange within the collaborative information integration platform keeps users updated on changes, activities, or related operations, and links users together in the research consortia.
• FIG. 1 depicts acollaboration system100 according to an embodiment of the present invention.Collaboration system100 includes acentral server102.Central server102 is in communication with a plurality oflocal servers104a,104b, . . . ,104N via anetwork106.Central server102 may also be in communication with one ormore users108. In at least one embodiment,users108 are also in communication with local servers104a-104N. In the same or alternative embodiments,users108 are in communication withcentral server102 and/or local servers104a-104N via a network, such asnetwork108. In this way,collaboration system100 is organized as a peer-to-peer network capable of sharing, transferring, and/or integrating data and/or information. As used herein, a device (e.g.,central server102, local servers104a-104N,users108, etc.) is in communication with another device if it is coupled to or otherwise connected via one or more networks, switches, wires, wireless networks, or the like such that the devices can transfer information, data, and/or metadata therebetween.
• Central server102 may be any appropriate server or computer, such as thecomputer300 discussed below with respect toFIG. 3. In this way,central server102 is a directory server for data and/or metadata for collaboration information (e.g., experiments, etc.).Central server102 keeps a directory of all experiments and transformations (e.g., steps or processes to perform experiments) by indexing metadata documents (e.g., extensible markup language (XML) documents, etc.), and also keeps a single hierarchical organization of all transformation instances from local servers104a-104N with links to the storage locations (e.g., uniform resource locator (URL), etc.) at local servers104a-104N. This central directory architecture ofcentral server102 provides an integrated view of experiments across thecollaboration system100.
• Local servers104a-104N may be any appropriate servers or computers, such as thecomputer300 discussed below with respect toFIG. 3. Incollaboration system100, local servers104a-104N host data (e.g., data related to experiments, etc.) and publish metadata documents tocentral server102, andcentral server102 provides a “virtual” view of all published data from multiple local servers. That is,central server102 enables each local server104a-104N in thecollaboration system100 to view data that is not stored at that particular local server104a-104N. Each local server104a-104N is a peer node incollaboration system100 that can work independently as a server for managing scientific experiments for a local institution or project.
• Network106 may be any appropriate wired and/or wireless global and/or local network. In at least one embodiment,network106 is and/or employs the Internet and the World Wide Web (Web). In this way,collaboration system100 is a Web-based system.
• Users108 are any users (e.g., computers, systems, servers, clients, etc.) permitted access to all or a portion ofcollaboration system100. For example, a local computer associated with one of the local servers104a-104N may be a user and may, in some embodiments have access to that local server and/orcentral server102.
• In operation,collaboration system100 uses metadata to describe experiments and their transformations, and link experiment data together through metadata. Metadata of experiments may be represented as XML documents. Accordingly, the metadata can be easily indexed and searched atcentral server102 and/or local servers104a-104N through standard XML query languages. The metadata document and the associated data files preserves all the information of a transformation. To provide maximum flexibility, the schemas of metadata can be customized at local servers104a-104N by describing them in a unified interface using XML. Of course, any other appropriate method of describing data using metadata may be used.
• As used herein, transformations are the fundamental object in scientific experiments which encodes an element of an experimental input/output process in enough detail that the process could be repeated by others. A transformation comprises a transformer, input, output, operator, and the setting parameters. Individual “atomic” level transformations can be linked into larger transformation pipelines atcentral server102.
• In operation,collaboration system100 enables local servers104a-104N to use common transformations in local experiments.Collaboration system100 also enables local servers104a-104N to share and co-author schemas throughcentral server102. This can reduce or eliminate data disagreement.
• FIG. 2 depicts a schematic diagram of aportion200 ofcollaboration system100 according to an embodiment of the present invention.Central server102 includes a number of collaborative engines such asmetadata engine202,schema engine204,hierarchy engine206,messaging engine208,annotation engine210, andtag engine212. Local servers104a-104N have analogous collaborative engines such as local metadata engines214a-214N, local schema engines216a-216N, local hierarchy engines218a-218N, local messaging engines220a-220N, local annotation engines222a-222N, and local tag engines224a-224N. Any appropriate combination of hardware, databases, and/or software may be used atcentral server102 to implementmetadata engine202,schema engine204,hierarchy engine206,messaging engine208,annotation engine210, andtag engine212. Similarly, any appropriate combination of hardware, databases, and/or software may be used at local servers104a-104N to implement local metadata engines214a-214N, local schema engines216a-216N, local hierarchy engines218a-218N, local messaging engines220a-220N, local annotation engines222a-222N, and local tag engines224a-224N. For example, a server or computer such ascomputer300, discussed below with respect toFIG. 3 may be used to provide these engines. One of skill in the art will recognize that, though discussed herein as separate components and individual engines ofcentral server102 and local servers104a-104N, these engines202-224 may be implemented logically and/or structurally as the same components in the respective servers. That is, for examplecentral server102 may use a single database (not shown) in coordination with one or more methods and/or software overlays to implement the features and functions of engines202-212, even if separate engines are not used. The various features and functions ofmetadata engine202,schema engine204,hierarchy engine206,messaging engine208,annotation engine210,tag engine212, local metadata engines214a-214N, local schema engines216a-216N, local hierarchy engines218a-218N, local messaging engines220a-220N, local annotation engines222a-222N, and local tag engines224a-224N are described below with respect to the use ofcollaboration system100. In other words, the engines are described herein to facilitate understanding of the invention and thecentral server102 and the local servers104a-104N are adapted to perform the functions described regardless of any division of hardware and/or associated software withincentral server102 and the local servers104a-104N.
• Data associated with experiments and metadata associated with the data is stored on local servers104a-104N. Additionally, metadata schemas that describe metadata documents, annotations on images, reviews on documents, and message repositories may also be stored on local servers104a-104N, such as at local metadata engines214a-214N and/or other local engines. The local servers104a-104N use a hierarchy within local hierarchy engines218a-218N that classifies data organizationally and local tag engines224a-224N (e.g., a local tag repository) that preserves information regarding previously used tags.
• Central server102 keeps a global repository of metadata atmetadata engine102, metadata schemas atschema engine204, hierarchies athierarchy engine206, and tags attag engine212.Central server102 also keeps a repository of messages generated by thecentral server102 atmessaging engine208. In at least one embodiment,central server102 does not store data but stores one or more addresses (e.g., URLs) pointing to the data stored at local servers104a-104N.
• Central server102 provides two major functionalities. It first provides a global but transparent view of all data published at local servers104a-104N (e.g., metadata, schemas, hierarchy, tags, etc.).Central server102 actively synchronizes information between local servers104a-104N andcentral server102. Thecentral server102 also serves as a hub for enabling collaborative information management such as collaborative management of schemas viaschema engine204 and local schema engines216a-216N and tags viatag engine212 and local tag engines224a-224N.
• Schema engine204 and local schema engines216a-216N communicate to facilitate sharing and co-authoring of schemas. This allows users to achieve common agreement to define data structures and semantics, thus minimizing the possibility of schema inconsistency. Users at local servers104a-104N can customize their own schemas for their experiments and transformations. Each schema of a transformation is represented as an XML document, and is associated with an owner at the respective local server104a-104N.
• Central server102 is a centralized repository that includes all schemas authored from local servers104a-104N. In at least one embodiment, only local users at local servers104a-104N can author schemas. Local servers104a-104N keep a cache of schemas at local schema engines216a-216N that will be used by local users, andcentral server102 keeps a collection of all schemas from local servers104a-104N atschema engine204. Local servers104a-104N synchronize schemas with thecentral server102. Cached schemas are refreshed when a local server104a-104N reboots or when the schema is viewed. New schemas are published tocentral server102 when there is an update of a schema at a local server104a-104N. In this way,central server102 serves as an information exchange hub, and only local servers104a-104N actively perform synchronization.
• When a user at a local server104a-104N wants to author data for their experiments, the user needs first to find an existing schema throughcentral server102 as a template. Accordingly, local servers104a-104N may communicate withcentral server102 to searchschema engine204. After searching, if an appropriate schema is found, that schema may be cached and use as a template for data authoring. In at least one embodiment, a Web authoring interface may be automatically generated based on the schema. If in appropriate schema is not located inschema engine204, a new schema may be created. In some embodiments, a Web-based schema authoring interface may be used at local servers104a-104N. After a new schema is created, the schema is automatically synchronized tocentral server102 atschema engine204.
• A user at local servers104a-104N will not be able to edit an existing schema with both addition and deletion operations unless the schema is currently only used by the local server associated with the user, no data is associated with this schema yet, and the user is the owner of the schema. If the schema is already used by multiple local servers104a-104N, schema editing is forbidden, but extension is possible. A user can extend a schema (e.g., invoke schema versioning) if the schema is close to the desired schema but needs extra additions. Incollaboration system100, only backward-compatible schema extension is possible. Thus, original schema elements can only be added and not removed. Schema versioning provides significant benefits for schema-based queries. Specifically, queries defined based on a latest versioned schema can always cover schemas of its ancestors. Additionally, if editing and extension of a schema is not possible or sufficient enough for a new transformation, users at local servers104a-104N can then create a new schema. Oncentral server102, each schema is preserved with information about local servers104a-104N that currently cache and use that schema. This will precisely account sharing information of transformation schemas.
• Users at local servers104a-104N (e.g., collaborators) may define schemas collaboratively or propose a new schema and then invite comments.Collaboration system100 provides the mechanism to collaboratively author schemas. Specifically, when a schema is authored at a local server104a-104N, the owner of the schema can set a status of this schema as “draft”, and invite other users at local servers104a-104N to edit the schema. After a draft schema is created, it is propagated tocentral server102. If a user is assigned as an author, the local server104a-104N used by this user will download this draft schema when synchronization is performed. When the user finishes the editing of the schema, the schema is sent back tocentral server102. The local server104a-104N of the schema owner will be updated with the different revisions of the schema, and the owner can continue to update the schema or pick up a revised version and set it as a “final” state. In at least one embodiment, only the owner can change a “draft” schema to “final.” When a draft schema is changed into the final state, the schema is not updatable any more by other users.
• Collaboration system100 also enables collaborative data tagging to improve search and organization of data. As discussed below,tag engine212 ofcentral server102 and local tag engines224a-224N may be used to facilitate data tagging by enabling addition of free tags and/or semantic tags (e.g., tags defined in a vocabulary), and sharing tags among multiple local servers104a-104N andusers108.
• Tagging is a method to enable users to add keywords to resources to improve search and organization. Incollaboration system100, data are hierarchically organized according to the organizational structure of a research consortium (e.g., a user of a local server104a-104N). In practice, it may be helpful to allow users to provide additional classification of data by assigning tags to documents. Each document can be flexibly annotated with one or more tags and tags themselves can be shared by users at both local servers104a-104N and thecentral server102. Generally, two types of tags are used—free tags and semantic tags. Free tags are arbitrary tags created by users and semantic tags are tags that are predefined in a controlled tag vocabulary.
• Free tagging is generally used by Web-based tagging systems where users can define arbitrary tags. Two categories of free tagging are organizational tagging, where users develop a personal standard to create new tags, and social tagging, where users try to express opinions regarding the quality of the resources. One issue for free tagging is that, since there is no common vocabulary among these tags, there can be semantic mismatch between tags. To combat this,collaboration system100 adds semantic tags from predefined ontology and/or controlled vocabulary, such as the National Cancer Institute (NCI) Enterprise Vocabulary Services (EVS) and Unified Medical Language System. By standardizing tags using such controlled vocabulary,collaboration system100, throughtag engine212 and/or local tag engines224a-224N, can provide a controlled set of semantic tags. Data can be categorized into multiple semantic groups, which makes it possible to express queries based on common semantics.
• Using a collaborative tag management system,tag engine212 and/or local tag engines224a-224N facilitates sharing of free tags among users throughcentral server102 and provides automatic tag lookup in a controlled vocabulary repository by caching previously retrieved tags incentral server102. While users at local servers104a-104N can define and manage their own tags individually using local tag engines224a-224N,central server102 andtag engine212 allow these tags to be shared by all collaborative users at other local servers104a-104N. Sharing common tags can minimize the number of tags, and make it possible to classify data and query data based on a small set of tags.Tag engine212 serves as a repository for shared free tags so a user at a local server104a-104N can automatically lookup a tag fromtag engine212 before creating a new tag.
• Tags are cached in thetag engine212 to exploit locality inherent to the subset of the vocabulary that is used within a group of researchers. Since a collaborative research consortium often focuses on solving a single problem, the vocabulary is quite smaller than a standardized vocabulary.Central server102 also provides a cached vocabulary repository. In this way, previously retrieved tags from the standardized vocabulary are shared among all users. Cached vocabulary oncentral server102 makes it very efficient for a user at local servers104a-104N to search for a tag in the vocabulary. Instead of searching for a tag at a remote large vocabulary, previously used tags in the tag cache repository oncentral server102 can be searched first.
• At each local server104a-104N, local tag engine224a-224N serves as a tag repository that manages all tags on this local server. Users can dynamically search and retrieve tags from a controlled vocabulary, as discussed above. Once a tag is defined on the local server (e.g., at local tag engine224a-224N), it will be automatically cached attag server212. When a user wants to associate a tag to its data,tag engine212 can be dynamically searched and tab may be selected through automatic tag lookup.
• Automatic tag lookup usingtag engine212 and/or local tag engines224a-224N may be used when a user wants to add a tag. In at least one embodiment, such an automatic lookup is based on Asynchronous JavaScript and XML (Ajax) technology. Accordingly, as a user types a keyword on a web page to search for a tag, there will be an automatic tag lookup from three resources—local tag engines224a-224N, the cached/sharedtag engine212, and a controlled vocabulary. The lookup will dynamically load a dropdown list of tags from which the user can choose a tag. By dynamically sending asynchronous tag queries to tag engines, users can immediately select a desired tag to label data instead of opening multiple browser windows to do separate searches. In this way, tags from a controlled vocabulary or shared repository are used with a convenient interface. Of course, one of skill in the art would recognize that any other appropriate method for tag interaction may be used.
• Collaboration system100 also provides a mechanism to support collaborative authoring of documents and collaborative reviewing of documents. Throughcentral server102 every metadata document can be reviewed and commented on. In at least one embodiment, comments from users to a transformation document are stored in a comment XML document atcentral server102. When a new comment is added, a message to the owner of the document is automatically generated and stored in a local message queue, such as at local messaging engines220a-220N. When the owner of the commented document logs intocollaboration system100, a message about this new comment may be automatically displayed.
• Central server102 facilitates a cooperative authoring environment based on invitation-based authoring. Each metadata document for a transformation has a primary owner, who is the creator of this document. After authoring the document, the owner can submit an invitation to the document. In at least one embodiment, the owner may use a form with the email and a temporary password, together with the subject and description of this invitation. When the invited user clicks on a link, the editing page for this document will be provided. The newly edited document may be temporarily stored in the same or a similar location atcentral server102. After a revision, a message may be added to a message queue targeted to the owner. If further authoring of the document is needed, the owner can then send a new invitation as described above.
• Even further,collaboration system100 enables medical image annotation. Generally, to annotate an image, a specific region of the image is highlighted and a textual note is added to describe this area.Central server102 uses a Scalable Vector Graphics (SVG) based approach to represent, author, and display annotations. SVG is a W3C standard language based on XML to define 2D vector graphics. SVG can be embedded into HTML pages and is natively supported by many conventional web browsers.
• Users at local servers104a-104N can interact with local annotation engines222a-222N to draw interesting regions of any shape supported by SVG, including point, rectangular, circle, polygon, polyline, etc. After a graphical object is drawn to define a region, textual annotations can be added accordingly. Multiple users can collaboratively author annotations and the corresponding annotations can be displayed as different layers, such as differentiated by colors. The technique is implemented by using JavaScript to directly manipulate SVG DOM and the web browser will render the image in real time. The Web-based annotation makes it very convenient for users to add annotations directly through the Web, and provides significant benefits. Instead of a standalone annotation application running on a workstation, where only local users can author, now users at disparate local servers104a-104N can browse images on the Web through collaboration system100 (e.g., usingannotation engine210, etc.) at any place and time. Such collaborative annotation can be very helpful to analyze complex images through aggregated intelligence. Both graphical and textual annotations are represented and stored in XML, such as atmetadata engine202 and/or local metadata engines214a-214N. These provide additional benefits in that annotations serve as additional metadata for images. Thus,central server102 provides knowledge based retrieval of images. Additionally, shapes and annotations are defined by SVG standard in XML. As a result,central server102 facilitates querying of both spatial objects and annotation text. Additionally, shapes authored by multiple users can be compared to find similarity, conflict, etc.
• Collaboration system100 also provides personalized message exchange usingmessaging engine208 atcentral server102 and/or local messaging engines220a-220N at local servers104a-104N. Messages are targeted to only related users. According to a priority, messages can be categorized as global messages, action messages, and notification messages.
• Global messages are messages published by community coordinators and/or administrators throughcentral server102 that announce progresses and/or events to local servers104a-104N. Action messages are messages that require a user to take certain action. For example, a user may be invited to co-author a metadata schema, or invited to make annotations on an image, as described above. Notification messages are related updates in the system. For example, as described above, a new schema may be extended by a previous schema owned by this user and a notification message may be broadcast.
• Messages are generated when certain user operations occur that will lead to enough interest to some users. A message includes the author, the target, creation date, and the message body. A message body is further differentiated according to the message types defined above. For example, the body of an action message includes a title, a description, an action type, an action URL, and authorization information.
• Each local server104a-104N andcentral server102 maintain a message repository, local messaging engines220a-220N andmessaging engine208 respectively. Each local messaging engine220a-220N maintains messages generated from local users targeted to local users, andmessaging engine208 maintains messages generated fromcentral server102, such as changes of schemas, invitation to author schemas, etc. Messages are generated through certain operations, such as announcement throughcentral server102, invitation for editing, reviewing or annotation, or notice of changes such as new schemas, new annotation, new comment, etc. Generated messages are sent to the messaging engine in the corresponding local server. When a user logs on to a local server104a-104N, messages targeted to this user are retrieved and filtered according to the generation date. They are then stored as personalized messages in this user's personal space.
• Of course, the above described functions of a collaborative data management system could be implemented in any appropriate form. For example, more than one computer or server may perform various functions ofcentral server102. Additionally, the above described methods of use may be implemented using a computer or machine readable medium, as described below with respect toFIG. 3.
• FIG. 3 is a schematic drawing of acomputer300 according to an embodiment of the invention.Computer300 may perform functions ofcentral server102 and/or local servers104a-104N, as described above.
• Computer300 contains aprocessor302 that controls the overall operation of thecomputer300 by executing computer program instructions, which define such operation. The computer program instructions may be stored in a storage device304 (e.g., magnetic disk, database, etc.) and loaded intomemory306 when execution of the computer program instructions is desired. Thus, applications for performing the herein-described operation are defined by the computer program instructions stored in thememory306 and/orstorage304 and controlled by theprocessor302 executing the computer program instructions. Thecomputer300 may also include one ormore network interfaces308 for communicating with other devices via a network. Thecomputer300 also includes input/output devices310 (e.g., display, keyboard, mouse, speakers, buttons, etc.) that enable user interaction with thecomputer300.Computer300 and/orprocessor302 may include one or more central processing units, read only memory (ROM) devices and/or random access memory (RAM) devices. One skilled in the art will recognize that an implementation of an actual controller could contain other components as well, and that the controller ofFIG. 3 is a high level representation of some of the components of such a controller for illustrative purposes.
• According to some embodiments of the present invention, instructions of a program (e.g., controller software) may be read intomemory306, such as from a ROM device to a RAM device or from a LAN adapter to a RAM device. Execution of sequences of the instructions in the program may cause thecomputer300 to perform one or more of the method steps described herein, such as those described above with respect to functions ofcentral server102 and/or local server104a-104N. In alternative embodiments, hard-wired circuitry or integrated circuits may be used in place of, or in combination with, software instructions for implementation of the processes of the present invention. Thus, embodiments of the present invention are not limited to any specific combination of hardware, firmware, and/or software. Thememory306 may store the software for thecomputer300, which may be adapted to execute the software program and thereby operate in accordance with the present invention and particularly in accordance with the methods described in detail above. However, it would be understood by one of ordinary skill in the art that the invention as described herein could be implemented in many different ways using a wide range of programming techniques as well as general purpose hardware sub-systems or dedicated controllers.
• Such programs may be stored in a compressed, uncompiled, and/or encrypted format. The programs furthermore may include program elements that may be generally useful, such as an operating system, a database management system, and device drivers for allowing the controller to interface with computer peripheral devices, and other equipment/components. Appropriate general purpose program elements are known to those skilled in the art, and need not be described in detail herein.
• The foregoing Detailed Description is to be understood as being in every respect illustrative and exemplary, but not restrictive, and the scope of the invention disclosed herein is not to be determined from the Detailed Description, but rather from the claims as interpreted according to the full breadth permitted by the patent laws. It is to be understood that the embodiments shown and described herein are only illustrative of the principles of the present invention and that various modifications may be implemented by those skilled in the art without departing from the scope and spirit of the invention. Those skilled in the art could implement various other feature combinations without departing from the scope and spirit of the invention.

Claims (21)

1. A system for collaborative data management comprising:

a central server comprising a plurality of collaborative engines; and

wherein the central server is adapted to receive information from a plurality of local servers and coordinate information transfer between the plurality of local servers and between the local servers and the central server.

2. The system ofclaim 1 further comprising:

a plurality of local servers, each comprising a plurality of local collaborative engines, the plurality of local servers adapted to transfer information to the central server and the plurality of local servers via the central server.

3. The system ofclaim 1 wherein the plurality of collaborative engines comprises at least one of a metadata engine, a schema engine, a hierarchy engine, and a messaging engine.

4. The system ofclaim 3 wherein at least one of the plurality of collaborative engines is a metadata engine adapted to receive metadata indicative of experiment data from the plurality of local servers, store the metadata, and index the stored metadata.

5. The system ofclaim 4 wherein the metadata engine is further adapted to link at least a portion of metadata indicative of experiment data stored at the plurality of local servers with at least another portion of metadata indicative of experiment data stored at the plurality of local servers.

6. The system ofclaim 3 wherein at least one of the plurality of collaborative engines is a schema engine adapted to receive one or more schemas from one or more of the plurality of local servers, store the one or more schemas received from the one or more of the plurality of local servers, and transmit the one or more schemas received from the one or more plurality of local servers to the plurality of local servers.

7. The system ofclaim 3 wherein at least one of the plurality of collaborative engines is a hierarchy engine adapted to provide hierarchical structure to data.

8. A method of collaborative data management comprising:

receiving, at a central server having a plurality of collaborative engines, information from a plurality of local servers; and

coordinating information transfer between the plurality of local servers and between the local servers and the central server.

9. The method ofclaim 8 further comprising:

transferring information to the central server and the plurality of local servers via the central server.

10. The method ofclaim 8 wherein the plurality of collaborative engines comprises at least one of a metadata engine, a schema engine, a hierarchy engine, and a messaging engine.

11. The method ofclaim 10 further comprising:

receiving, at a metadata engine, metadata indicative of experiment data from the plurality of local servers;

storing the metadata; and

indexing the stored metadata.

12. The method ofclaim 11 further comprising:

linking at least a portion of metadata indicative of experiment data stored at the plurality of local servers with at least another portion of metadata indicative of experiment data stored at the plurality of local servers.

13. The method ofclaim 10 further comprising:

receiving, at a schema engine, one or more schemas from one or more of the plurality of local servers;

storing the one or more schemas received from the one or more of the plurality of local servers; and

transmitting the one or more schemas received from the one or more plurality of local servers to the plurality of local servers.

14. The method ofclaim 10 further comprising:

providing hierarchical structure to data with a hierarchy engine.

15. A machine readable medium having program instructions stored thereon, the instructions capable of execution by a processor and defining the steps of:

receiving, at a central server having a plurality of collaborative engines, information from a plurality of local servers; and

coordinating information transfer between the plurality of local servers and between the local servers and the central server.

16. The machine readable medium ofclaim 15, wherein the instructions further define the step of:

transferring information to the central server and the plurality of local servers via the central server.

17. The machine readable medium ofclaim 15, wherein the instructions further define the step of:

providing as the plurality of collaborative engines a metadata engine, a schema engine, a hierarchy engine, and a messaging engine.

18. The machine readable medium ofclaim 17, wherein the instructions further define the steps of:

receiving, at a metadata engine, metadata indicative of experiment data from the plurality of local servers;

storing the metadata; and

indexing the stored metadata.

19. The machine readable medium ofclaim 18, wherein the instructions further define the step of:

linking at least a portion of metadata indicative of experiment data stored at the plurality of local servers with at least another portion of metadata indicative of experiment data stored at the plurality of local servers.

20. The machine readable medium ofclaim 17, wherein the instructions further define the steps of:

receiving, at a schema engine, one or more schemas from one or more of the plurality of local servers;

storing the one or more schemas received from the one or more of the plurality of local servers; and

transmitting the one or more schemas received from the one or more plurality of local servers to the plurality of local servers.

21. The machine readable medium ofclaim 17, wherein the instructions further define the step of:

providing hierarchical structure to data with a hierarchy engine.

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